Overview

Dataset statistics

Number of variables29
Number of observations24786
Missing cells9563
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 MiB
Average record size in memory232.0 B

Variable types

Numeric17
Categorical12

Alerts

categoria is highly imbalanced (82.1%)Imbalance
garantia is highly imbalanced (82.7%)Imbalance
forma_de_pago is highly imbalanced (59.0%)Imbalance
reestructurado is highly imbalanced (98.9%)Imbalance
ocupacion is highly imbalanced (62.5%)Imbalance
tipo_contrato is highly imbalanced (68.1%)Imbalance
clase is highly imbalanced (86.9%)Imbalance
tipo_vivienda has 4372 (17.6%) missing valuesMissing
tipo_contrato has 5191 (20.9%) missing valuesMissing
cedula is uniformly distributedUniform
cedula has unique valuesUnique
tiempo_desembolso has 1102 (4.4%) zerosZeros
diasmora has 15060 (60.8%) zerosZeros
egreso_total has 4683 (18.9%) zerosZeros
antiguedad_empresa has 5554 (22.4%) zerosZeros
personas has 9305 (37.5%) zerosZeros
aportes has 3128 (12.6%) zerosZeros
antiguedad_entidad has 418 (1.7%) zerosZeros

Reproduction

Analysis started2024-03-12 12:38:07.996614
Analysis finished2024-03-12 12:39:05.218559
Duration57.22 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

cedula
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct24786
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12393.5
Minimum1
Maximum24786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.8 KiB
2024-03-12T07:39:05.626680image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1240.25
Q16197.25
median12393.5
Q318589.75
95-th percentile23546.75
Maximum24786
Range24785
Interquartile range (IQR)12392.5

Descriptive statistics

Standard deviation7155.2462
Coefficient of variation (CV)0.57733862
Kurtosis-1.2
Mean12393.5
Median Absolute Deviation (MAD)6196.5
Skewness2.7536731 Ă— 10-17
Sum3.0718529 Ă— 108
Variance51197548
MonotonicityNot monotonic
2024-03-12T07:39:06.105022image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6754 1
 
< 0.1%
13535 1
 
< 0.1%
13573 1
 
< 0.1%
13577 1
 
< 0.1%
13578 1
 
< 0.1%
13579 1
 
< 0.1%
13580 1
 
< 0.1%
13581 1
 
< 0.1%
13582 1
 
< 0.1%
13583 1
 
< 0.1%
Other values (24776) 24776
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
24786 1
< 0.1%
24785 1
< 0.1%
24784 1
< 0.1%
24783 1
< 0.1%
24782 1
< 0.1%
24781 1
< 0.1%
24780 1
< 0.1%
24779 1
< 0.1%
24778 1
< 0.1%
24777 1
< 0.1%

oficina
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size193.8 KiB
Santa Fe
4977 
Chapinero
4720 
Usme
3660 
San CristĂ³bal
3090 
Usaquen
2488 
Other values (4)
5851 

Length

Max length13
Median length9
Mean length7.8997821
Min length4

Characters and Unicode

Total characters195804
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSanta Fe
2nd rowUsaquen
3rd rowChapinero
4th rowSanta Fe
5th rowChapinero

Common Values

ValueCountFrequency (%)
Santa Fe 4977
20.1%
Chapinero 4720
19.0%
Usme 3660
14.8%
San CristĂ³bal 3090
12.5%
Usaquen 2488
10.0%
Tunjuelito 1937
 
7.8%
Kennedy 1597
 
6.4%
Tunal 1465
 
5.9%
Bosa 852
 
3.4%

Length

2024-03-12T07:39:06.550452image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T07:39:06.869265image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
santa 4977
15.1%
fe 4977
15.1%
chapinero 4720
14.4%
usme 3660
11.1%
san 3090
9.4%
cristĂ³bal 3090
9.4%
usaquen 2488
7.6%
tunjuelito 1937
 
5.9%
kennedy 1597
 
4.9%
tunal 1465
 
4.5%

Most occurring characters

ValueCountFrequency (%)
a 25659
 
13.1%
n 21871
 
11.2%
e 20976
 
10.7%
s 10090
 
5.2%
t 10004
 
5.1%
i 9747
 
5.0%
S 8067
 
4.1%
8067
 
4.1%
u 7827
 
4.0%
C 7810
 
4.0%
Other values (17) 65686
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 154884
79.1%
Uppercase Letter 32853
 
16.8%
Space Separator 8067
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 25659
16.6%
n 21871
14.1%
e 20976
13.5%
s 10090
 
6.5%
t 10004
 
6.5%
i 9747
 
6.3%
u 7827
 
5.1%
r 7810
 
5.0%
o 7509
 
4.8%
l 6492
 
4.2%
Other values (9) 26899
17.4%
Uppercase Letter
ValueCountFrequency (%)
S 8067
24.6%
C 7810
23.8%
U 6148
18.7%
F 4977
15.1%
T 3402
10.4%
K 1597
 
4.9%
B 852
 
2.6%
Space Separator
ValueCountFrequency (%)
8067
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 187737
95.9%
Common 8067
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 25659
13.7%
n 21871
 
11.6%
e 20976
 
11.2%
s 10090
 
5.4%
t 10004
 
5.3%
i 9747
 
5.2%
S 8067
 
4.3%
u 7827
 
4.2%
C 7810
 
4.2%
r 7810
 
4.2%
Other values (16) 57876
30.8%
Common
ValueCountFrequency (%)
8067
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 192714
98.4%
None 3090
 
1.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 25659
13.3%
n 21871
 
11.3%
e 20976
 
10.9%
s 10090
 
5.2%
t 10004
 
5.2%
i 9747
 
5.1%
S 8067
 
4.2%
8067
 
4.2%
u 7827
 
4.1%
C 7810
 
4.1%
Other values (16) 62596
32.5%
None
ValueCountFrequency (%)
Ă³ 3090
100.0%

categoria
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size193.8 KiB
A
23332 
B
 
843
E
 
304
C
 
162
D
 
145

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24786
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowE
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
A 23332
94.1%
B 843
 
3.4%
E 304
 
1.2%
C 162
 
0.7%
D 145
 
0.6%

Length

2024-03-12T07:39:07.427186image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T07:39:07.568959image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
a 23332
94.1%
b 843
 
3.4%
e 304
 
1.2%
c 162
 
0.7%
d 145
 
0.6%

Most occurring characters

ValueCountFrequency (%)
A 23332
94.1%
B 843
 
3.4%
E 304
 
1.2%
C 162
 
0.7%
D 145
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 24786
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 23332
94.1%
B 843
 
3.4%
E 304
 
1.2%
C 162
 
0.7%
D 145
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 24786
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 23332
94.1%
B 843
 
3.4%
E 304
 
1.2%
C 162
 
0.7%
D 145
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 23332
94.1%
B 843
 
3.4%
E 304
 
1.2%
C 162
 
0.7%
D 145
 
0.6%

tiempo_desembolso
Real number (ℝ)

ZEROS 

Distinct67
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5409909
Minimum-11
Maximum68
Zeros1102
Zeros (%)4.4%
Negative1
Negative (%)< 0.1%
Memory size193.8 KiB
2024-03-12T07:39:07.733534image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-11
5-th percentile1
Q13
median8
Q314
95-th percentile23
Maximum68
Range79
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.1048327
Coefficient of variation (CV)0.84947495
Kurtosis6.2701694
Mean9.5409909
Median Absolute Deviation (MAD)5
Skewness1.8086797
Sum236483
Variance65.688314
MonotonicityNot monotonic
2024-03-12T07:39:07.918891image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1768
 
7.1%
5 1740
 
7.0%
3 1722
 
6.9%
1 1633
 
6.6%
4 1526
 
6.2%
6 1424
 
5.7%
8 1352
 
5.5%
7 1271
 
5.1%
9 1216
 
4.9%
0 1102
 
4.4%
Other values (57) 10032
40.5%
ValueCountFrequency (%)
-11 1
 
< 0.1%
0 1102
4.4%
1 1633
6.6%
2 1768
7.1%
3 1722
6.9%
4 1526
6.2%
5 1740
7.0%
6 1424
5.7%
7 1271
5.1%
8 1352
5.5%
ValueCountFrequency (%)
68 1
 
< 0.1%
67 4
 
< 0.1%
66 1
 
< 0.1%
65 4
 
< 0.1%
64 3
 
< 0.1%
63 3
 
< 0.1%
62 4
 
< 0.1%
61 9
< 0.1%
60 15
0.1%
59 7
< 0.1%

monto
Real number (ℝ)

Distinct645
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3213142
Minimum101083
Maximum61800000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.8 KiB
2024-03-12T07:39:08.118845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum101083
5-th percentile800000
Q11500000
median2500000
Q34000000
95-th percentile8000000
Maximum61800000
Range61698917
Interquartile range (IQR)2500000

Descriptive statistics

Standard deviation3052970.7
Coefficient of variation (CV)0.95015118
Kurtosis56.979316
Mean3213142
Median Absolute Deviation (MAD)1000000
Skewness5.3728547
Sum7.9640938 Ă— 1010
Variance9.3206298 Ă— 1012
MonotonicityNot monotonic
2024-03-12T07:39:08.335536image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000000 3313
 
13.4%
3000000 2342
 
9.4%
1000000 1888
 
7.6%
1500000 1808
 
7.3%
4000000 1503
 
6.1%
2500000 1488
 
6.0%
5000000 1292
 
5.2%
6000000 720
 
2.9%
3500000 706
 
2.8%
1200000 608
 
2.5%
Other values (635) 9118
36.8%
ValueCountFrequency (%)
101083 1
< 0.1%
139347 1
< 0.1%
146792 1
< 0.1%
191143 1
< 0.1%
219687 1
< 0.1%
232719 1
< 0.1%
237054 1
< 0.1%
241834 1
< 0.1%
277069 1
< 0.1%
277967 1
< 0.1%
ValueCountFrequency (%)
61800000 1
 
< 0.1%
61000000 1
 
< 0.1%
57000000 1
 
< 0.1%
52000000 4
< 0.1%
50000000 2
 
< 0.1%
45000000 5
< 0.1%
44000000 1
 
< 0.1%
42000000 1
 
< 0.1%
40000000 2
 
< 0.1%
39510444 1
 
< 0.1%

saldo
Real number (ℝ)

Distinct12659
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2380017.4
Minimum19
Maximum59312409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.8 KiB
2024-03-12T07:39:08.560144image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile275344.5
Q1876867
median1669316.5
Q32960204.8
95-th percentile6744576
Maximum59312409
Range59312390
Interquartile range (IQR)2083337.8

Descriptive statistics

Standard deviation2641900.3
Coefficient of variation (CV)1.110034
Kurtosis52.303771
Mean2380017.4
Median Absolute Deviation (MAD)955031.5
Skewness5.0301935
Sum5.8991112 Ă— 1010
Variance6.9796372 Ă— 1012
MonotonicityNot monotonic
2024-03-12T07:39:08.754567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000000 160
 
0.6%
1000000 122
 
0.5%
1500000 114
 
0.5%
1935679 106
 
0.4%
3000000 100
 
0.4%
1870044 88
 
0.4%
1803306 72
 
0.3%
954047 66
 
0.3%
2829846 60
 
0.2%
2887422 56
 
0.2%
Other values (12649) 23842
96.2%
ValueCountFrequency (%)
19 1
< 0.1%
35 1
< 0.1%
2941 1
< 0.1%
6801 1
< 0.1%
8757 1
< 0.1%
12426 1
< 0.1%
26932 1
< 0.1%
28168 1
< 0.1%
38870 1
< 0.1%
42242 1
< 0.1%
ValueCountFrequency (%)
59312409 1
< 0.1%
56549783 1
< 0.1%
48112641 1
< 0.1%
45530067 1
< 0.1%
42782716 1
< 0.1%
39717871 1
< 0.1%
38734501 1
< 0.1%
38505226 1
< 0.1%
37525257 1
< 0.1%
36171750 1
< 0.1%

plazo
Real number (ℝ)

Distinct84
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.003591
Minimum2
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.8 KiB
2024-03-12T07:39:08.932180image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile15
Q118
median24
Q336
95-th percentile48
Maximum180
Range178
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.567857
Coefficient of variation (CV)0.41308478
Kurtosis12.924694
Mean28.003591
Median Absolute Deviation (MAD)6
Skewness2.0375796
Sum694097
Variance133.81532
MonotonicityNot monotonic
2024-03-12T07:39:09.151928image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 5605
22.6%
36 5458
22.0%
18 3715
15.0%
30 2254
9.1%
15 1694
 
6.8%
48 1643
 
6.6%
12 1072
 
4.3%
21 766
 
3.1%
27 681
 
2.7%
33 491
 
2.0%
Other values (74) 1407
 
5.7%
ValueCountFrequency (%)
2 4
 
< 0.1%
3 2
 
< 0.1%
4 3
 
< 0.1%
5 1
 
< 0.1%
6 32
0.1%
7 8
 
< 0.1%
8 27
0.1%
9 12
 
< 0.1%
10 42
0.2%
11 6
 
< 0.1%
ValueCountFrequency (%)
180 4
 
< 0.1%
144 1
 
< 0.1%
125 1
 
< 0.1%
120 39
0.2%
112 1
 
< 0.1%
108 2
 
< 0.1%
105 1
 
< 0.1%
103 1
 
< 0.1%
96 7
 
< 0.1%
94 1
 
< 0.1%

tasa
Real number (ℝ)

Distinct61
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1714661
Minimum0.1625
Maximum3.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.8 KiB
2024-03-12T07:39:09.372301image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.1625
5-th percentile2.17
Q12.1742
median2.1742
Q32.1742
95-th percentile2.1742
Maximum3.3
Range3.1375
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.065143665
Coefficient of variation (CV)0.029999853
Kurtosis239.25723
Mean2.1714661
Median Absolute Deviation (MAD)0
Skewness0.46037458
Sum53821.959
Variance0.0042436971
MonotonicityNot monotonic
2024-03-12T07:39:09.585407image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1742 23066
93.1%
2.17 578
 
2.3%
2 219
 
0.9%
2.18 211
 
0.9%
2.0833 84
 
0.3%
1.95 78
 
0.3%
2.1867 68
 
0.3%
2.02 67
 
0.3%
2.1217 62
 
0.3%
2.0834 47
 
0.2%
Other values (51) 306
 
1.2%
ValueCountFrequency (%)
0.1625 1
 
< 0.1%
0.5 1
 
< 0.1%
1 15
 
0.1%
1.5 22
 
0.1%
1.8133 1
 
< 0.1%
1.95 78
 
0.3%
1.9725 27
 
0.1%
1.9783 46
 
0.2%
2 219
0.9%
2.02 67
 
0.3%
ValueCountFrequency (%)
3.3 18
0.1%
3.25 5
 
< 0.1%
3.2083 2
 
< 0.1%
3.1667 5
 
< 0.1%
3.15 4
 
< 0.1%
2.95 3
 
< 0.1%
2.9083 1
 
< 0.1%
2.875 1
 
< 0.1%
2.85 1
 
< 0.1%
2.8333 1
 
< 0.1%

cuota
Real number (ℝ)

Distinct8313
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145241.79
Minimum22247
Maximum2080188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.8 KiB
2024-03-12T07:39:09.802103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum22247
5-th percentile60988
Q195181
median121054
Q3164076.25
95-th percentile298228
Maximum2080188
Range2057941
Interquartile range (IQR)68895.25

Descriptive statistics

Standard deviation94230.521
Coefficient of variation (CV)0.64878379
Kurtosis50.265431
Mean145241.79
Median Absolute Deviation (MAD)35926.5
Skewness4.8353305
Sum3.5999631 Ă— 109
Variance8.8793912 Ă— 109
MonotonicityNot monotonic
2024-03-12T07:39:10.022437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107849 134
 
0.5%
107899 131
 
0.5%
107842 129
 
0.5%
108441 114
 
0.5%
108754 97
 
0.4%
107877 97
 
0.4%
67765 97
 
0.4%
121054 87
 
0.4%
107840 83
 
0.3%
107898 82
 
0.3%
Other values (8303) 23735
95.8%
ValueCountFrequency (%)
22247 1
< 0.1%
26312 1
< 0.1%
28682 1
< 0.1%
28689 1
< 0.1%
28690 1
< 0.1%
28719 1
< 0.1%
30183 1
< 0.1%
30357 1
< 0.1%
30504 1
< 0.1%
31723 1
< 0.1%
ValueCountFrequency (%)
2080188 1
< 0.1%
1887469 1
< 0.1%
1821775 1
< 0.1%
1743432 1
< 0.1%
1713100 1
< 0.1%
1657846 1
< 0.1%
1488098 1
< 0.1%
1430866 1
< 0.1%
1415445 1
< 0.1%
1376580 1
< 0.1%

garantia
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size193.8 KiB
P
24145 
R
 
641

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24786
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP
2nd rowP
3rd rowP
4th rowP
5th rowR

Common Values

ValueCountFrequency (%)
P 24145
97.4%
R 641
 
2.6%

Length

2024-03-12T07:39:10.201544image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T07:39:10.317228image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
p 24145
97.4%
r 641
 
2.6%

Most occurring characters

ValueCountFrequency (%)
P 24145
97.4%
R 641
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 24786
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 24145
97.4%
R 641
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 24786
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 24145
97.4%
R 641
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 24145
97.4%
R 641
 
2.6%

diasmora
Real number (ℝ)

ZEROS 

Distinct402
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.893367
Minimum0
Maximum1784
Zeros15060
Zeros (%)60.8%
Negative0
Negative (%)0.0%
Memory size193.8 KiB
2024-03-12T07:39:10.484862image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q310
95-th percentile40
Maximum1784
Range1784
Interquartile range (IQR)10

Descriptive statistics

Standard deviation71.817455
Coefficient of variation (CV)5.16919
Kurtosis203.7449
Mean13.893367
Median Absolute Deviation (MAD)0
Skewness12.824392
Sum344361
Variance5157.7469
MonotonicityDecreasing
2024-03-12T07:39:10.785529image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15060
60.8%
4 587
 
2.4%
5 528
 
2.1%
2 453
 
1.8%
3 442
 
1.8%
10 384
 
1.5%
9 383
 
1.5%
8 371
 
1.5%
7 365
 
1.5%
6 343
 
1.4%
Other values (392) 5870
 
23.7%
ValueCountFrequency (%)
0 15060
60.8%
1 1
 
< 0.1%
2 453
 
1.8%
3 442
 
1.8%
4 587
 
2.4%
5 528
 
2.1%
6 343
 
1.4%
7 365
 
1.5%
8 371
 
1.5%
9 383
 
1.5%
ValueCountFrequency (%)
1784 1
< 0.1%
1758 1
< 0.1%
1677 1
< 0.1%
1670 1
< 0.1%
1621 1
< 0.1%
1587 1
< 0.1%
1440 1
< 0.1%
1424 1
< 0.1%
1416 1
< 0.1%
1407 1
< 0.1%

forma_de_pago
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size193.8 KiB
1
22747 
2
 
2039

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24786
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 22747
91.8%
2 2039
 
8.2%

Length

2024-03-12T07:39:10.968812image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T07:39:11.107598image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 22747
91.8%
2 2039
 
8.2%

Most occurring characters

ValueCountFrequency (%)
1 22747
91.8%
2 2039
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24786
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 22747
91.8%
2 2039
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Common 24786
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 22747
91.8%
2 2039
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 22747
91.8%
2 2039
 
8.2%

reestructurado
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size193.8 KiB
2
24761 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24786
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 24761
99.9%
1 25
 
0.1%

Length

2024-03-12T07:39:11.252038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T07:39:11.430166image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 24761
99.9%
1 25
 
0.1%

Most occurring characters

ValueCountFrequency (%)
2 24761
99.9%
1 25
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24786
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 24761
99.9%
1 25
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 24786
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 24761
99.9%
1 25
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 24761
99.9%
1 25
 
0.1%

edad
Real number (ℝ)

Distinct61
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.081941
Minimum19
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.8 KiB
2024-03-12T07:39:11.601975image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile25
Q132
median40
Q349
95-th percentile62
Maximum83
Range64
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.147799
Coefficient of variation (CV)0.27135521
Kurtosis-0.54128261
Mean41.081941
Median Absolute Deviation (MAD)8
Skewness0.43378159
Sum1018257
Variance124.27341
MonotonicityNot monotonic
2024-03-12T07:39:11.818319image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 916
 
3.7%
35 905
 
3.7%
37 869
 
3.5%
36 847
 
3.4%
39 839
 
3.4%
33 818
 
3.3%
34 803
 
3.2%
41 799
 
3.2%
31 787
 
3.2%
40 777
 
3.1%
Other values (51) 16426
66.3%
ValueCountFrequency (%)
19 1
 
< 0.1%
20 27
 
0.1%
21 94
 
0.4%
22 173
 
0.7%
23 287
1.2%
24 359
1.4%
25 504
2.0%
26 549
2.2%
27 647
2.6%
28 705
2.8%
ValueCountFrequency (%)
83 1
 
< 0.1%
80 1
 
< 0.1%
77 1
 
< 0.1%
76 1
 
< 0.1%
75 3
 
< 0.1%
74 6
 
< 0.1%
73 3
 
< 0.1%
72 15
 
0.1%
71 24
0.1%
70 39
0.2%

ocupacion
Categorical

IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size193.8 KiB
Empleado
19788 
Jubilado
2285 
Independiente
2034 
Pensionado
 
383
Desempleado
 
222
Other values (2)
 
74

Length

Max length13
Median length8
Mean length8.4769225
Min length8

Characters and Unicode

Total characters210109
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEmpleado
2nd rowEmpleado
3rd rowEmpleado
4th rowEmpleado
5th rowIndependiente

Common Values

ValueCountFrequency (%)
Empleado 19788
79.8%
Jubilado 2285
 
9.2%
Independiente 2034
 
8.2%
Pensionado 383
 
1.5%
Desempleado 222
 
0.9%
Ama de Casa 71
 
0.3%
Estudiante 3
 
< 0.1%

Length

2024-03-12T07:39:12.201687image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T07:39:12.358566image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
empleado 19788
79.4%
jubilado 2285
 
9.2%
independiente 2034
 
8.2%
pensionado 383
 
1.5%
desempleado 222
 
0.9%
ama 71
 
0.3%
de 71
 
0.3%
casa 71
 
0.3%
estudiante 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 29047
13.8%
d 26820
12.8%
o 23061
11.0%
a 22894
10.9%
l 22295
10.6%
p 22044
10.5%
m 20081
9.6%
E 19791
9.4%
n 6871
 
3.3%
i 4705
 
2.2%
Other values (11) 12500
5.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 185110
88.1%
Uppercase Letter 24857
 
11.8%
Space Separator 142
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 29047
15.7%
d 26820
14.5%
o 23061
12.5%
a 22894
12.4%
l 22295
12.0%
p 22044
11.9%
m 20081
10.8%
n 6871
 
3.7%
i 4705
 
2.5%
u 2288
 
1.2%
Other values (3) 5004
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
E 19791
79.6%
J 2285
 
9.2%
I 2034
 
8.2%
P 383
 
1.5%
D 222
 
0.9%
A 71
 
0.3%
C 71
 
0.3%
Space Separator
ValueCountFrequency (%)
142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 209967
99.9%
Common 142
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 29047
13.8%
d 26820
12.8%
o 23061
11.0%
a 22894
10.9%
l 22295
10.6%
p 22044
10.5%
m 20081
9.6%
E 19791
9.4%
n 6871
 
3.3%
i 4705
 
2.2%
Other values (10) 12358
5.9%
Common
ValueCountFrequency (%)
142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210109
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 29047
13.8%
d 26820
12.8%
o 23061
11.0%
a 22894
10.9%
l 22295
10.6%
p 22044
10.5%
m 20081
9.6%
E 19791
9.4%
n 6871
 
3.3%
i 4705
 
2.2%
Other values (11) 12500
5.9%

nivel_educativo
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size193.8 KiB
Bachillerato
11686 
Universitario
4726 
TecnolĂ³gico
4590 
Primaria
3368 
Técnico
 
262
Other values (2)
 
154

Length

Max length13
Median length12
Mean length11.37852
Min length7

Characters and Unicode

Total characters282028
Distinct characters23
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBachillerato
2nd rowBachillerato
3rd rowTécnico
4th rowBachillerato
5th rowUniversitario

Common Values

ValueCountFrequency (%)
Bachillerato 11686
47.1%
Universitario 4726
19.1%
TecnolĂ³gico 4590
 
18.5%
Primaria 3368
 
13.6%
Técnico 262
 
1.1%
Ninguno 148
 
0.6%
Postgrado 6
 
< 0.1%

Length

2024-03-12T07:39:12.551698image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T07:39:12.718359image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
bachillerato 11686
47.1%
universitario 4726
19.1%
tecnolĂ³gico 4590
 
18.5%
primaria 3368
 
13.6%
técnico 262
 
1.1%
ninguno 148
 
0.6%
postgrado 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 37600
13.3%
a 34840
12.4%
l 27962
9.9%
r 27880
9.9%
o 26014
9.2%
c 21390
7.6%
e 21002
7.4%
t 16418
 
5.8%
B 11686
 
4.1%
h 11686
 
4.1%
Other values (13) 45550
16.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 257242
91.2%
Uppercase Letter 24786
 
8.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 37600
14.6%
a 34840
13.5%
l 27962
10.9%
r 27880
10.8%
o 26014
10.1%
c 21390
8.3%
e 21002
8.2%
t 16418
6.4%
h 11686
 
4.5%
n 9874
 
3.8%
Other values (8) 22576
8.8%
Uppercase Letter
ValueCountFrequency (%)
B 11686
47.1%
T 4852
19.6%
U 4726
19.1%
P 3374
 
13.6%
N 148
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 282028
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 37600
13.3%
a 34840
12.4%
l 27962
9.9%
r 27880
9.9%
o 26014
9.2%
c 21390
7.6%
e 21002
7.4%
t 16418
 
5.8%
B 11686
 
4.1%
h 11686
 
4.1%
Other values (13) 45550
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 277176
98.3%
None 4852
 
1.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 37600
13.6%
a 34840
12.6%
l 27962
10.1%
r 27880
10.1%
o 26014
9.4%
c 21390
7.7%
e 21002
7.6%
t 16418
5.9%
B 11686
 
4.2%
h 11686
 
4.2%
Other values (11) 40698
14.7%
None
ValueCountFrequency (%)
Ă³ 4590
94.6%
Ă© 262
 
5.4%

ingreso_total
Real number (ℝ)

Distinct7936
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1068714.5
Minimum0
Maximum15100000
Zeros238
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size193.8 KiB
2024-03-12T07:39:12.971291image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile380000
Q1600000
median863982
Q31285000
95-th percentile2446600
Maximum15100000
Range15100000
Interquartile range (IQR)685000

Descriptive statistics

Standard deviation785496.91
Coefficient of variation (CV)0.73499227
Kurtosis31.470932
Mean1068714.5
Median Absolute Deviation (MAD)313982
Skewness3.8626758
Sum2.6489158 Ă— 1010
Variance6.170054 Ă— 1011
MonotonicityNot monotonic
2024-03-12T07:39:13.218056image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600000 519
 
2.1%
1200000 510
 
2.1%
800000 508
 
2.0%
700000 423
 
1.7%
1500000 416
 
1.7%
1000000 415
 
1.7%
900000 330
 
1.3%
500000 323
 
1.3%
1300000 272
 
1.1%
1100000 268
 
1.1%
Other values (7926) 20802
83.9%
ValueCountFrequency (%)
0 238
1.0%
1 18
 
0.1%
100 1
 
< 0.1%
1000 1
 
< 0.1%
20000 2
 
< 0.1%
50000 6
 
< 0.1%
60000 1
 
< 0.1%
80000 2
 
< 0.1%
82000 1
 
< 0.1%
100000 21
 
0.1%
ValueCountFrequency (%)
15100000 1
< 0.1%
15000000 1
< 0.1%
13200000 1
< 0.1%
12300000 1
< 0.1%
12250000 1
< 0.1%
12000000 1
< 0.1%
10614000 1
< 0.1%
10500000 1
< 0.1%
10000000 2
< 0.1%
9900000 1
< 0.1%

egreso_total
Real number (ℝ)

ZEROS 

Distinct1867
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean360320.26
Minimum0
Maximum11800000
Zeros4683
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size193.8 KiB
2024-03-12T07:39:13.440933image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1120000
median300000
Q3500000
95-th percentile1000000
Maximum11800000
Range11800000
Interquartile range (IQR)380000

Descriptive statistics

Standard deviation407422.84
Coefficient of variation (CV)1.1307242
Kurtosis73.721673
Mean360320.26
Median Absolute Deviation (MAD)192000
Skewness5.3154815
Sum8.9308981 Ă— 109
Variance1.6599337 Ă— 1011
MonotonicityNot monotonic
2024-03-12T07:39:13.798480image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4683
18.9%
200000 1699
 
6.9%
300000 1662
 
6.7%
400000 994
 
4.0%
150000 989
 
4.0%
250000 943
 
3.8%
500000 912
 
3.7%
100000 827
 
3.3%
350000 632
 
2.5%
600000 619
 
2.5%
Other values (1857) 10826
43.7%
ValueCountFrequency (%)
0 4683
18.9%
1 16
 
0.1%
100 1
 
< 0.1%
1000 1
 
< 0.1%
20000 2
 
< 0.1%
25000 1
 
< 0.1%
30000 10
 
< 0.1%
32000 1
 
< 0.1%
38000 2
 
< 0.1%
40000 6
 
< 0.1%
ValueCountFrequency (%)
11800000 1
< 0.1%
10000000 1
< 0.1%
8150000 1
< 0.1%
8000000 1
< 0.1%
7000000 1
< 0.1%
6200000 1
< 0.1%
6180000 1
< 0.1%
6000000 1
< 0.1%
5500000 2
< 0.1%
5300000 1
< 0.1%

estrato
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.816711
Minimum0
Maximum6
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size193.8 KiB
2024-03-12T07:39:13.966141image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79818026
Coefficient of variation (CV)0.28337314
Kurtosis1.3351731
Mean2.816711
Median Absolute Deviation (MAD)0
Skewness0.6143378
Sum69815
Variance0.63709172
MonotonicityNot monotonic
2024-03-12T07:39:14.097366image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 13222
53.3%
2 7612
30.7%
4 2444
 
9.9%
5 778
 
3.1%
1 623
 
2.5%
6 106
 
0.4%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 623
 
2.5%
2 7612
30.7%
3 13222
53.3%
4 2444
 
9.9%
5 778
 
3.1%
6 106
 
0.4%
ValueCountFrequency (%)
6 106
 
0.4%
5 778
 
3.1%
4 2444
 
9.9%
3 13222
53.3%
2 7612
30.7%
1 623
 
2.5%
0 1
 
< 0.1%

antiguedad_empresa
Real number (ℝ)

ZEROS 

Distinct48
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7613976
Minimum-1
Maximum46
Zeros5554
Zeros (%)22.4%
Negative61
Negative (%)0.2%
Memory size193.8 KiB
2024-03-12T07:39:14.283767image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q11
median6
Q311
95-th percentile26
Maximum46
Range47
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.0802384
Coefficient of variation (CV)1.0410803
Kurtosis1.408668
Mean7.7613976
Median Absolute Deviation (MAD)5
Skewness1.3334429
Sum192374
Variance65.290252
MonotonicityNot monotonic
2024-03-12T07:39:14.499346image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 5554
22.4%
3 1567
 
6.3%
5 1432
 
5.8%
2 1407
 
5.7%
4 1401
 
5.7%
8 1312
 
5.3%
6 1273
 
5.1%
7 1254
 
5.1%
9 1084
 
4.4%
11 876
 
3.5%
Other values (38) 7626
30.8%
ValueCountFrequency (%)
-1 61
 
0.2%
0 5554
22.4%
1 824
 
3.3%
2 1407
 
5.7%
3 1567
 
6.3%
4 1401
 
5.7%
5 1432
 
5.8%
6 1273
 
5.1%
7 1254
 
5.1%
8 1312
 
5.3%
ValueCountFrequency (%)
46 1
 
< 0.1%
45 1
 
< 0.1%
44 2
 
< 0.1%
43 2
 
< 0.1%
42 5
 
< 0.1%
41 5
 
< 0.1%
40 8
 
< 0.1%
39 8
 
< 0.1%
38 13
0.1%
37 25
0.1%

estado_civil
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size193.8 KiB
Casado
12135 
Soltero
7601 
UniĂ³n Libre
2150 
Separado
1701 
Viudo
 
1189

Length

Max length12
Median length11
Mean length6.8320826
Min length5

Characters and Unicode

Total characters169340
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUniĂ³n Libre
2nd rowSoltero
3rd rowSoltero
4th rowCasado
5th rowCasado

Common Values

ValueCountFrequency (%)
Casado 12135
49.0%
Soltero 7601
30.7%
UniĂ³n Libre 2150
 
8.7%
Separado 1701
 
6.9%
Viudo 1189
 
4.8%
EclesiĂ¡stico 10
 
< 0.1%

Length

2024-03-12T07:39:14.683706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T07:39:14.832605image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
casado 12135
45.1%
soltero 7601
28.2%
uniĂ³n 2150
 
8.0%
libre 2150
 
8.0%
separado 1701
 
6.3%
viudo 1189
 
4.4%
eclesiĂ¡stico 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 30237
17.9%
a 27672
16.3%
d 15025
8.9%
s 12155
7.2%
C 12135
7.2%
e 11462
 
6.8%
r 11452
 
6.8%
S 9302
 
5.5%
l 7611
 
4.5%
t 7611
 
4.5%
Other values (13) 24678
14.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 140254
82.8%
Uppercase Letter 26936
 
15.9%
Space Separator 2150
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 30237
21.6%
a 27672
19.7%
d 15025
10.7%
s 12155
8.7%
e 11462
 
8.2%
r 11452
 
8.2%
l 7611
 
5.4%
t 7611
 
5.4%
i 5509
 
3.9%
n 4300
 
3.1%
Other values (6) 7220
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
C 12135
45.1%
S 9302
34.5%
U 2150
 
8.0%
L 2150
 
8.0%
V 1189
 
4.4%
E 10
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 167190
98.7%
Common 2150
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 30237
18.1%
a 27672
16.6%
d 15025
9.0%
s 12155
7.3%
C 12135
7.3%
e 11462
 
6.9%
r 11452
 
6.8%
S 9302
 
5.6%
l 7611
 
4.6%
t 7611
 
4.6%
Other values (12) 22528
13.5%
Common
ValueCountFrequency (%)
2150
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 167180
98.7%
None 2160
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 30237
18.1%
a 27672
16.6%
d 15025
9.0%
s 12155
7.3%
C 12135
7.3%
e 11462
 
6.9%
r 11452
 
6.9%
S 9302
 
5.6%
l 7611
 
4.6%
t 7611
 
4.6%
Other values (11) 22518
13.5%
None
ValueCountFrequency (%)
Ă³ 2150
99.5%
Ă¡ 10
 
0.5%

sexo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size193.8 KiB
Masculino
12500 
Femenino
12286 

Length

Max length9
Median length9
Mean length8.504317
Min length8

Characters and Unicode

Total characters210788
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMasculino
2nd rowMasculino
3rd rowFemenino
4th rowMasculino
5th rowMasculino

Common Values

ValueCountFrequency (%)
Masculino 12500
50.4%
Femenino 12286
49.6%

Length

2024-03-12T07:39:14.996085image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T07:39:15.150227image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
masculino 12500
50.4%
femenino 12286
49.6%

Most occurring characters

ValueCountFrequency (%)
n 37072
17.6%
i 24786
11.8%
o 24786
11.8%
e 24572
11.7%
M 12500
 
5.9%
a 12500
 
5.9%
s 12500
 
5.9%
c 12500
 
5.9%
u 12500
 
5.9%
l 12500
 
5.9%
Other values (2) 24572
11.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 186002
88.2%
Uppercase Letter 24786
 
11.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 37072
19.9%
i 24786
13.3%
o 24786
13.3%
e 24572
13.2%
a 12500
 
6.7%
s 12500
 
6.7%
c 12500
 
6.7%
u 12500
 
6.7%
l 12500
 
6.7%
m 12286
 
6.6%
Uppercase Letter
ValueCountFrequency (%)
M 12500
50.4%
F 12286
49.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 210788
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 37072
17.6%
i 24786
11.8%
o 24786
11.8%
e 24572
11.7%
M 12500
 
5.9%
a 12500
 
5.9%
s 12500
 
5.9%
c 12500
 
5.9%
u 12500
 
5.9%
l 12500
 
5.9%
Other values (2) 24572
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210788
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 37072
17.6%
i 24786
11.8%
o 24786
11.8%
e 24572
11.7%
M 12500
 
5.9%
a 12500
 
5.9%
s 12500
 
5.9%
c 12500
 
5.9%
u 12500
 
5.9%
l 12500
 
5.9%
Other values (2) 24572
11.7%

personas
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2311789
Minimum0
Maximum25
Zeros9305
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size193.8 KiB
2024-03-12T07:39:15.265824image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum25
Range25
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.237067
Coefficient of variation (CV)1.0047825
Kurtosis5.38155
Mean1.2311789
Median Absolute Deviation (MAD)1
Skewness1.0346785
Sum30516
Variance1.5303347
MonotonicityNot monotonic
2024-03-12T07:39:15.399121image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 9305
37.5%
1 5965
24.1%
2 5368
21.7%
3 3076
 
12.4%
4 841
 
3.4%
5 191
 
0.8%
6 31
 
0.1%
7 7
 
< 0.1%
25 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 9305
37.5%
1 5965
24.1%
2 5368
21.7%
3 3076
 
12.4%
4 841
 
3.4%
5 191
 
0.8%
6 31
 
0.1%
7 7
 
< 0.1%
8 1
 
< 0.1%
25 1
 
< 0.1%
ValueCountFrequency (%)
25 1
 
< 0.1%
8 1
 
< 0.1%
7 7
 
< 0.1%
6 31
 
0.1%
5 191
 
0.8%
4 841
 
3.4%
3 3076
 
12.4%
2 5368
21.7%
1 5965
24.1%
0 9305
37.5%

tipo_vivienda
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing4372
Missing (%)17.6%
Memory size193.8 KiB
Propia
10326 
Familiar
6366 
Arrendada
3722 

Length

Max length9
Median length6
Mean length7.1706672
Min length6

Characters and Unicode

Total characters146382
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFamiliar
2nd rowPropia
3rd rowFamiliar
4th rowFamiliar
5th rowFamiliar

Common Values

ValueCountFrequency (%)
Propia 10326
41.7%
Familiar 6366
25.7%
Arrendada 3722
 
15.0%
(Missing) 4372
17.6%

Length

2024-03-12T07:39:15.574861image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T07:39:15.714601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
propia 10326
50.6%
familiar 6366
31.2%
arrendada 3722
 
18.2%

Most occurring characters

ValueCountFrequency (%)
a 30502
20.8%
r 24136
16.5%
i 23058
15.8%
P 10326
 
7.1%
o 10326
 
7.1%
p 10326
 
7.1%
d 7444
 
5.1%
F 6366
 
4.3%
m 6366
 
4.3%
l 6366
 
4.3%
Other values (3) 11166
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 125968
86.1%
Uppercase Letter 20414
 
13.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 30502
24.2%
r 24136
19.2%
i 23058
18.3%
o 10326
 
8.2%
p 10326
 
8.2%
d 7444
 
5.9%
m 6366
 
5.1%
l 6366
 
5.1%
e 3722
 
3.0%
n 3722
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
P 10326
50.6%
F 6366
31.2%
A 3722
 
18.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 146382
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 30502
20.8%
r 24136
16.5%
i 23058
15.8%
P 10326
 
7.1%
o 10326
 
7.1%
p 10326
 
7.1%
d 7444
 
5.1%
F 6366
 
4.3%
m 6366
 
4.3%
l 6366
 
4.3%
Other values (3) 11166
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 146382
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 30502
20.8%
r 24136
16.5%
i 23058
15.8%
P 10326
 
7.1%
o 10326
 
7.1%
p 10326
 
7.1%
d 7444
 
5.1%
F 6366
 
4.3%
m 6366
 
4.3%
l 6366
 
4.3%
Other values (3) 11166
 
7.6%

tipo_contrato
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing5191
Missing (%)20.9%
Memory size193.8 KiB
Término Indefinido
17061 
Término Definido
2234 
Servicios
 
157
Jubilados
 
143

Length

Max length18
Median length18
Mean length17.634192
Min length9

Characters and Unicode

Total characters345542
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTérmino Indefinido
2nd rowTérmino Indefinido
3rd rowTérmino Indefinido
4th rowTérmino Indefinido
5th rowTérmino Indefinido

Common Values

ValueCountFrequency (%)
Término Indefinido 17061
68.8%
Término Definido 2234
 
9.0%
Servicios 157
 
0.6%
Jubilados 143
 
0.6%
(Missing) 5191
 
20.9%

Length

2024-03-12T07:39:15.862550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T07:39:15.999989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
término 19295
49.6%
indefinido 17061
43.9%
definido 2234
 
5.7%
servicios 157
 
0.4%
jubilados 143
 
0.4%

Most occurring characters

ValueCountFrequency (%)
i 58342
16.9%
n 55651
16.1%
o 38890
11.3%
d 36499
10.6%
r 19452
 
5.6%
e 19452
 
5.6%
T 19295
 
5.6%
Ă© 19295
 
5.6%
f 19295
 
5.6%
19295
 
5.6%
Other values (12) 40076
11.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 287357
83.2%
Uppercase Letter 38890
 
11.3%
Space Separator 19295
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 58342
20.3%
n 55651
19.4%
o 38890
13.5%
d 36499
12.7%
r 19452
 
6.8%
e 19452
 
6.8%
Ă© 19295
 
6.7%
f 19295
 
6.7%
m 19295
 
6.7%
s 300
 
0.1%
Other values (6) 886
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
T 19295
49.6%
I 17061
43.9%
D 2234
 
5.7%
S 157
 
0.4%
J 143
 
0.4%
Space Separator
ValueCountFrequency (%)
19295
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 326247
94.4%
Common 19295
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 58342
17.9%
n 55651
17.1%
o 38890
11.9%
d 36499
11.2%
r 19452
 
6.0%
e 19452
 
6.0%
T 19295
 
5.9%
Ă© 19295
 
5.9%
f 19295
 
5.9%
m 19295
 
5.9%
Other values (11) 20781
 
6.4%
Common
ValueCountFrequency (%)
19295
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 326247
94.4%
None 19295
 
5.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 58342
17.9%
n 55651
17.1%
o 38890
11.9%
d 36499
11.2%
r 19452
 
6.0%
e 19452
 
6.0%
T 19295
 
5.9%
f 19295
 
5.9%
19295
 
5.9%
m 19295
 
5.9%
Other values (11) 20781
 
6.4%
None
ValueCountFrequency (%)
Ă© 19295
100.0%

aportes
Real number (ℝ)

ZEROS 

Distinct819
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84506.373
Minimum0
Maximum1463000
Zeros3128
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size193.8 KiB
2024-03-12T07:39:16.163482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115500
median31000
Q3155000
95-th percentile212500
Maximum1463000
Range1463000
Interquartile range (IQR)139500

Descriptive statistics

Standard deviation96940.637
Coefficient of variation (CV)1.14714
Kurtosis12.968855
Mean84506.373
Median Absolute Deviation (MAD)31000
Skewness2.2477617
Sum2.094575 Ă— 109
Variance9.3974871 Ă— 109
MonotonicityNot monotonic
2024-03-12T07:39:16.348894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15500 4629
18.7%
0 3128
 
12.6%
155000 1600
 
6.5%
6000 1396
 
5.6%
16000 1092
 
4.4%
170000 968
 
3.9%
21500 827
 
3.3%
166000 798
 
3.2%
131000 773
 
3.1%
143000 628
 
2.5%
Other values (809) 8947
36.1%
ValueCountFrequency (%)
0 3128
12.6%
6000 1396
5.6%
7000 74
 
0.3%
9000 1
 
< 0.1%
10000 144
 
0.6%
11357 1
 
< 0.1%
11854 1
 
< 0.1%
12000 25
 
0.1%
12500 1
 
< 0.1%
13000 22
 
0.1%
ValueCountFrequency (%)
1463000 1
 
< 0.1%
1300000 4
< 0.1%
1143429 1
 
< 0.1%
1125000 1
 
< 0.1%
1115000 2
< 0.1%
1000000 1
 
< 0.1%
975000 1
 
< 0.1%
963000 1
 
< 0.1%
920000 1
 
< 0.1%
891000 2
< 0.1%

numero_creditos
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1631163
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.8 KiB
2024-03-12T07:39:16.512362image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum15
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.232528
Coefficient of variation (CV)0.56979277
Kurtosis1.6966851
Mean2.1631163
Median Absolute Deviation (MAD)1
Skewness1.0615887
Sum53615
Variance1.5191253
MonotonicityNot monotonic
2024-03-12T07:39:16.649832image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 9799
39.5%
2 6420
25.9%
3 4743
19.1%
4 2708
 
10.9%
5 900
 
3.6%
6 162
 
0.7%
7 29
 
0.1%
9 10
 
< 0.1%
8 7
 
< 0.1%
12 4
 
< 0.1%
Other values (3) 4
 
< 0.1%
ValueCountFrequency (%)
1 9799
39.5%
2 6420
25.9%
3 4743
19.1%
4 2708
 
10.9%
5 900
 
3.6%
6 162
 
0.7%
7 29
 
0.1%
8 7
 
< 0.1%
9 10
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
12 4
 
< 0.1%
11 1
 
< 0.1%
10 2
 
< 0.1%
9 10
 
< 0.1%
8 7
 
< 0.1%
7 29
 
0.1%
6 162
 
0.7%
5 900
 
3.6%
4 2708
10.9%

antiguedad_entidad
Real number (ℝ)

ZEROS 

Distinct32
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2287582
Minimum0
Maximum49
Zeros418
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size193.8 KiB
2024-03-12T07:39:16.798750image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q35
95-th percentile8
Maximum49
Range49
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6921005
Coefficient of variation (CV)0.83378822
Kurtosis11.82301
Mean3.2287582
Median Absolute Deviation (MAD)1
Skewness2.1768567
Sum80028
Variance7.2474053
MonotonicityNot monotonic
2024-03-12T07:39:16.966387image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 7580
30.6%
2 5156
20.8%
3 3318
13.4%
4 1982
 
8.0%
5 1678
 
6.8%
6 1577
 
6.4%
7 1376
 
5.6%
8 737
 
3.0%
0 418
 
1.7%
9 386
 
1.6%
Other values (22) 578
 
2.3%
ValueCountFrequency (%)
0 418
 
1.7%
1 7580
30.6%
2 5156
20.8%
3 3318
13.4%
4 1982
 
8.0%
5 1678
 
6.8%
6 1577
 
6.4%
7 1376
 
5.6%
8 737
 
3.0%
9 386
 
1.6%
ValueCountFrequency (%)
49 1
 
< 0.1%
39 1
 
< 0.1%
38 1
 
< 0.1%
36 1
 
< 0.1%
31 1
 
< 0.1%
29 1
 
< 0.1%
27 1
 
< 0.1%
25 3
< 0.1%
23 5
< 0.1%
22 3
< 0.1%

clase
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size193.8 KiB
Bueno
24334 
Malo
 
452

Length

Max length5
Median length5
Mean length4.9817639
Min length4

Characters and Unicode

Total characters123478
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMalo
2nd rowMalo
3rd rowMalo
4th rowMalo
5th rowMalo

Common Values

ValueCountFrequency (%)
Bueno 24334
98.2%
Malo 452
 
1.8%

Length

2024-03-12T07:39:17.115309image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T07:39:17.247579image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
bueno 24334
98.2%
malo 452
 
1.8%

Most occurring characters

ValueCountFrequency (%)
o 24786
20.1%
B 24334
19.7%
u 24334
19.7%
e 24334
19.7%
n 24334
19.7%
M 452
 
0.4%
a 452
 
0.4%
l 452
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98692
79.9%
Uppercase Letter 24786
 
20.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 24786
25.1%
u 24334
24.7%
e 24334
24.7%
n 24334
24.7%
a 452
 
0.5%
l 452
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
B 24334
98.2%
M 452
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 123478
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 24786
20.1%
B 24334
19.7%
u 24334
19.7%
e 24334
19.7%
n 24334
19.7%
M 452
 
0.4%
a 452
 
0.4%
l 452
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123478
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 24786
20.1%
B 24334
19.7%
u 24334
19.7%
e 24334
19.7%
n 24334
19.7%
M 452
 
0.4%
a 452
 
0.4%
l 452
 
0.4%

Interactions

2024-03-12T07:39:01.553958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:12.083791image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:14.819489image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:17.421311image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:20.145834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:23.028874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:26.066837image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:29.728301image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:32.395450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:35.646446image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:38.875776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:42.244798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:46.324815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:49.999012image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:53.405520image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:55.905512image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:58.621398image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:01.921752image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:12.248180image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:14.970208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:17.568746image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:20.307287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:23.185542image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:26.433429image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:29.871528image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:32.682943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:35.880231image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:39.007041image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:42.478254image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:46.532908image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:50.120656image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:53.555060image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:56.053382image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:58.738025image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:02.069432image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:12.418169image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:15.136781image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:17.724892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:20.485498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:23.365045image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:26.920431image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:30.120762image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:32.878447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:36.179931image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:39.160206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:42.702369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:46.758006image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:50.273783image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:53.730185image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:56.184599image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:58.884925image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:02.185057image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:12.570783image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:15.303311image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:17.868861image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:20.635236image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:23.518448image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:27.252159image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:30.262528image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:33.025365image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:36.378031image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:39.307980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:42.900712image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:47.001036image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:50.406046image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:53.945898image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:56.355349image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:59.017162image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:02.325073image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:12.722705image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:15.453304image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:18.002068image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:20.801743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:23.681819image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:27.571297image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:30.386600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:33.269916image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:36.560805image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:39.455875image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:43.161363image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:47.139144image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:50.615601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:54.114003image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:56.486570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:59.145949image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:02.470441image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:12.884796image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:15.603265image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:18.152972image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:20.952050image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:23.830420image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:27.752220image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:30.542662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:33.428347image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:36.693120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:39.609990image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:43.427775image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:47.279175image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:50.847147image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:54.305847image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:56.618882image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:59.271220image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:02.586967image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:13.087461image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:15.754378image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:18.299718image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:21.101731image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:23.968339image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:27.919448image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:30.679162image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:33.586409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:36.824321image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:39.742242image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:43.652884image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:47.491137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:51.023551image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:54.436126image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:56.755337image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:59.387893image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:02.744977image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:13.220215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:15.908583image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:18.438350image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:21.250956image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:24.184921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:28.250647image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:30.795706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:33.719676image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:37.063189image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:39.873440image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:43.891902image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:47.699090image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:51.242617image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:54.573955image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:56.879931image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:59.519154image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:02.953809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:13.371766image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:16.069720image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:18.585576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:21.435875image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:24.352255image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:28.471190image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:30.943602image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:33.860535image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:37.235715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:40.026559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:44.217889image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:48.143213image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:51.464961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:54.721445image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:57.005167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:59.651381image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:03.083702image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:13.519931image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:16.219415image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:18.885544image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:21.604829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:24.506794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:28.621072image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:31.079050image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:34.011586image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:37.528224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:40.181894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:44.510083image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:48.492962image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:51.656737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:54.853798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:57.152038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:59.863435image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:03.236861image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:13.687837image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:16.369361image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:19.035521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:21.790048image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:24.688451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:28.762935image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:31.213609image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:34.244756image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:37.746042image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:40.428719image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:44.672084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:48.638734image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:51.965228image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:54.985990image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:57.284290image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:00.021127image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:03.369054image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:13.854279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:16.520018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:19.194995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:22.063190image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:24.873761image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:28.895322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:31.366542image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:34.395405image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:37.935217image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:40.653098image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:44.841891image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:48.824154image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:52.132356image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:55.130448image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:57.585230image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:00.237684image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:03.516948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:14.031739image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:16.685809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:19.337735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:22.239445image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:25.040295image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:29.063631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:31.612391image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:34.578410image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:38.057322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:41.018406image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:45.091140image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:49.031392image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:52.356466image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:55.254637image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:57.887199image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:00.461985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:03.632758image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:14.188899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:16.819057image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:19.482341image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:22.437514image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:25.184315image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:29.195103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:31.745522image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:34.828279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:38.195213image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:41.278506image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:45.343984image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:49.155252image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:52.565592image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:55.386906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:58.087180image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:00.670881image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:03.770033image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:14.337748image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:16.969937image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:19.618949image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:22.614728image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:25.333879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:29.327541image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:31.862078image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:35.002643image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:38.410336image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:41.511841image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:45.585413image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:49.290694image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:52.764405image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:55.502487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:58.219449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:00.987608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:03.917876image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:14.505420image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:17.118384image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:19.785020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:22.763548image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:25.502887image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:29.466331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:32.080576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:35.178040image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:38.593934image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:41.761548image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:45.835522image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:49.542960image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:52.999735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:55.656595image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:58.350690image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:01.135306image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:04.050094image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:14.673018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:17.285813image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:19.935576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:22.894743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:25.814445image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:29.596023image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:32.265873image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:35.402979image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:38.735058image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:42.020304image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:46.084435image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:49.788915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:53.215718image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:55.788925image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:38:58.495662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-12T07:39:01.335584image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-12T07:39:04.311246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-12T07:39:04.868645image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

cedulaoficinacategoriatiempo_desembolsomontosaldoplazotasacuotagarantiadiasmoraforma_de_pagoreestructuradoedadocupacionnivel_educativoingreso_totalegreso_totalestratoantiguedad_empresaestado_civilsexopersonastipo_viviendatipo_contratoaportesnumero_creditosantiguedad_entidadclase
06754Santa FeE63500000.0411775.0183.166736868P17841230EmpleadoBachillerato372315.00.036UniĂ³n LibreMasculino0NaNNaN69000.016Malo
117922UsaquenE652000000.01563604.0243.2083120761P17581238EmpleadoBachillerato900000.00.028SolteroMasculino0NaNNaN0.016Malo
2167ChapineroE60800000.0658843.0183.166758990P16771257EmpleadoTécnico800000.00.0414SolteroFemenino0NaNNaN82000.015Malo
323093Santa FeE57800000.0744568.0213.208352945P16701228EmpleadoBachillerato325000.080000.038CasadoMasculino0FamiliarTérmino Indefinido82000.015Malo
416530ChapineroE5814153655.013567368.0482.8750547318R16211246IndependienteUniversitario2500000.01500000.050CasadoMasculino2PropiaNaN300000.017Malo
523667UsaquenE621000000.0569689.0183.166773737P15871238EmpleadoBachillerato401280.0167000.035SeparadoFemenino0FamiliarTérmino Indefinido69000.017Malo
622882Santa FeE521500000.01168465.0153.3000128392P14401256DesempleadoBachillerato0.00.030CasadoMasculino3FamiliarNaN95000.015Malo
715797ChapineroE511000000.0825327.0183.300074565P14241239EmpleadoTecnolĂ³gico410000.00.039SolteroFemenino0NaNNaN0.015Malo
822336UsmeE5410000000.09931812.01202.1742247418R14161246IndependienteBachillerato1200000.00.030CasadoMasculino0NaNNaN200000.015Malo
95403UsaquenE619642432.06980780.0362.8333430745R14071244IndependienteBachillerato1600000.00.020UniĂ³n LibreFemenino0NaNNaN373900.027Malo
cedulaoficinacategoriatiempo_desembolsomontosaldoplazotasacuotagarantiadiasmoraforma_de_pagoreestructuradoedadocupacionnivel_educativoingreso_totalegreso_totalestratoantiguedad_empresaestado_civilsexopersonastipo_viviendatipo_contratoaportesnumero_creditosantiguedad_entidadclase
2477614UsaquenA51600000.01211941.0182.1742108426P02222EmpleadoTecnolĂ³gico415000.0100000.021SolteroMasculino1FamiliarTĂ©rmino Definido15500.011Bueno
2477713UsmeA81200000.0867291.0242.174264761P01237IndependienteNinguno846000.0350000.010UniĂ³n LibreMasculino5ArrendadaTĂ©rmino Indefinido155000.037Bueno
2477812UsmeA31600000.01413681.0212.174295746P01227EmpleadoBachillerato609000.080008.042SolteroMasculino0FamiliarTérmino Definido15500.011Bueno
2477911UsaquenA26000000.05774787.0362.1742242048P01232IndependienteBachillerato2450000.01500000.030CasadoMasculino2FamiliarNaN212000.044Bueno
2478010UsaquenA51500000.01243988.0302.170067312P02236EmpleadoBachillerato670000.0350000.028CasadoMasculino3PropiaTérmino Indefinido21500.032Bueno
247817UsaquenA206000000.02719389.0362.0000210640P02238EmpleadoBachillerato1200000.0500000.0311CasadoMasculino3FamiliarTérmino Indefinido6000.012Bueno
247826BosaA812000000.010726957.0482.0834397891R01239EmpleadoUniversitario5741200.02170147.067UniĂ³n LibreMasculino1FamiliarTĂ©rmino Indefinido131000.034Bueno
247835TunjuelitoA142000000.0966688.0242.1742108602P01238EmpleadoUniversitario1047000.0200000.038SolteroMasculino0PropiaTérmino Indefinido0.037Bueno
247844Santa FeA5900000.0684264.0182.174260991P01239EmpleadoBachillerato309000.00.0311SolteroMasculino0NaNTérmino Indefinido0.011Bueno
247853UsaquenA3600000.0464362.0122.174257366P01230EmpleadoTecnolĂ³gico806000.0533600.033CasadoMasculino2ArrendadaTĂ©rmino Indefinido15500.011Bueno